Getting too deeply into statistics is like trying to quench a thirst with salty water. The angst of facing mortality has no remedy in probability.
Paul Kalanithi, When Breath Becomes Air
An interview with Georgina Sturge, a statistician in the House of Commons Library. The first quote deals with an issue I have heard about before, although I do not know further details. If it is not true it will be.
Sturge also cites the cautionary tale of Carmen Reinhart and Kenneth Rogoff, two Harvard economists whose 2010 model purported to show that, if a country’s debt-to-GDP ratio exceeded 90 per cent, the risk of a negative impact on long-term growth became significant. This research was used by George Osborne to justify austerity, on the grounds that if the UK didn’t get its debt under control, the economy would shrink. The only problem? Reinhart and Rogoff had made an error, missing off five rows of data on their spreadsheet, negating their conclusion about the risk of negative growth. By the time this was spotted, austerity was well under way and its effects are still being felt now. And while it’s a stretch to say that David Cameron and Osborne would have pursued a different course had the mistake been caught earlier, bad data gave them cover for their economic programme.
If we want our politicians to use data more responsibly, Georgina Sturge argues, we need to invest in better ways of collecting it. It’s perverse that we know more about the performance of Premier League footballers than how many children are out of school.
Not really. It’s just money.
‘Statisticians have already overrun every branch of science with a rapidity of conquest rivalled only by Attila, Mohammed, and the Colorado beetle’
Maurice Kendall (1942): On the future of statistics. JRSA 105; 69-80.
Yes, that Maurice Kendall.
It seems to me that when it comes to statistics — and the powerful role of statistics in understanding both the natural and the unnatural world — that the old guys thought harder and deeper, understanding the world better than many of their more vocal successors. And that is without mentioning the barking of the medic-would-be-statistician brigade.
Defining the appropriate probability space is often a non-trivial bit of statistics. It is often where you have to end up leaving statistics and formal reasoning behind. The following quote puts this in a more bracing manner.
There are no lobby groups for companies that do not exist.
The same goes for research and so much of what makes the future captivating.
We are living in dark times, and since I have been sifting through the ashes of a career, it is no surprise that failures signal through like radioactive tracers. Below is one.
Through most of my career I have been interested in the relation between science and medicine. In truth, if what matters is what you think about in the shower, I have been more interested in the relation between science and medicine than I have been interested in either activity in isolation. If I were to use a phrase to describe my focus, although it is a term that I would not have used then, I am interested in the epistemological foundations of medical practice. Pompous, I agree. I could use another phrase: what makes medicine and doctors useful? Thinking about statistical inference is a part of this topic, but there is much more to explore.
These issues became closer to my consciousness soon after I moved to Edinburgh. My ideas about what was going on were not shared by many locally, and I was nervous about going public in person rather than in print at a Symposium hosted by the Royal College of Physicians of Edinburgh. My nervousness was well founded: whilst I liked my abstract, my talk went down badly. Not least because it was truly dreadful (and the evident failure still rankles). Jan Vandenbroucke, one of the other speakers and somebody whose work I greatly admire (his paper in the Lancet, Homoeopathy trials: Going nowhere. [Lancet.1997;350:824], was to me the most important paper published in the Lancet in the 1990s), said some kind words to me afterwards, muttering that I had tried to say far too much to an audience that was ill prepared for my speculations. All true, but he was just being kind. It was worse than that.
Anyway, some tidying up deep in my hard drive surfaced the abstract. I still like it, but it is a shame that at the appropriate time I was unable to explain why.
JAMES LIND SYMPOSIUM: From scurvy to systematic reviews and clinical guidelines: how can clinical research lead to better patient care? (31-10-2003, RCPE Edinburgh)
There are three great branches of science: theory, experiment, and computation. (Nick Trefethen)
Advance in the mid-third of the twentieth century, the golden age of medical research, was predicated on earlier discoveries in the nineteenth century in both physiology and medicinal chemistry (1). Genetics dominated biology in the latter third of the twentieth century and many believe changes in medical practice will owe much to genetics over the next third-century (1). I disagree, and I will give an alternative view more credence: in 30 years’ time we will look back more to Neumann and Morgenstern than we will to Watson and Crick. What the Nobel laureate Herbert Simon referred to as The Sciences of the Artificial (2), subjects which have largely been peripheral to medicine, will become central.
Over the last 20 years we have seen the first (largely inadequate, I would add) attempts to explicitly demarcate methods of obtaining and promulgating knowledge about clinical practice (3,4). This has usually taken the form of proselytising a particular set of terms – systematic reviews, evidence-based practice, guidelines and the like, terms that have little to commend them or rigour. What is interesting, however, is that they reflect a long overdue renaissance of interest with the practice of medicine and medical epistemology.
The change of emphasis from the natural to the artificial is being driven by a number of forces, mostly extraneous to biomedicine: the increasing instrumental role of science in medicine and society; the increase in corporatisation of knowledge, whether by private corporations or monopsonistic institutions like the NHS (5); the rising costs of healthcare; and a remaining inability to frame questions with broad support about how to chose between alternative disease states at the level of society (6,7).
I will try to illustrate some of these issues by the use of three examples. First, the widespread use of a mode of statistical inference largely ill-suited to medicine, namely Neyman-Pearson hypothesis testing (decision-making), and the way in which this paradigm has been used to undermine expert opinion (8). Second, I will argue that we need to think much harder about clinical practice and fashion a more appropriate theoretical underpinning for clinical behaviour. Third, I will suggest how UK medical schools, in so far as they remain interested in clinical practice, should look to alternative models, perhaps business and law schools, for ideas of how they should operate (2).
Afterword. The symposium used structured abstracts, a habit that might have a place somewhere in this galaxy, but out of choice I would prefer to live in another one. Anyway, in the published version, it reads:
A fair cop.